Management of Perianal Fistulizing Crohn's Disease According to Principles of Wound Repair
Bibliographic record
Abstract
BACKGROUND: Perianal fistulizing Crohn's disease (PFCD) is a challenging and debilitating phenotype of Crohn's disease that can negatively affect quality of life. Studies have begun to uncover the physiologic mechanisms involved in wound repair as it relates to PFCD and how aberrations in these mechanisms may contribute to fistula persistence. AIMS: To review the physiologic and pathophysiologic mechanisms of wound repair in PFCD and how specific therapeutic strategies may impact their outcomes. METHODS: We reviewed the latest published literature on wound repair as it relates to PFCD. RESULTS: Wound repair can be categorised into three overlapping biological phases: localised inflammation, cell recruitment/proliferation and tissue remodelling. Each is tightly regulated since insufficient or excessive activation can result in, respectively, chronic wounds and fibrotic tissue, both of which can impair organ function. In PFCD, the outcomes of wound repair include restitution (complete healing), epithelialisation and chronic wounds. Treatment of PFCD should take into consideration the distinct phases of wound repair. Therefore, the ability to differentiate between each phase of wound repair and their outcomes may help physicians deliver the most effective treatment strategy at the most appropriate time. CONCLUSIONS: This review provides a comprehensive overview of the phases of wound repair and specific treatment strategies for each to provide clinicians with a rational framework for managing PFCD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".